Contextual information plays a pivotal role in the semantic segmentation of remote sensing imagery (RSI) due to the imbalanced distributions and ubiquitous intra-class variants. The emergence of the transformer intrigues the revolution of vision tasks with its impressive scalability in establishing long-range dependencies. However, the local patterns, such as inherent structures and spatial details, are broken with the tokenization of the transformer. Therefore, the ICTNet is devised to confront the deficiencies mentioned above. Principally, ICTNet inherits the encoder–decoder architecture. First of all, Swin Transformer blocks (STBs) and convolution blocks (CBs) are deployed and interlaced, accompanied by encoded feature aggregation modules (EFAs) in the encoder stage. This design allows the network to learn the local patterns and distant dependencies and their interactions simultaneously. Moreover, multiple DUpsamplings (DUPs) followed by decoded feature aggregation modules (DFAs) form the decoder of ICTNet. Specifically, the transformation and upsampling loss are shrunken while recovering features. Together with the devised encoder and decoder, the well-rounded context is captured and contributes to the inference most. Extensive experiments are conducted on the ISPRS Vaihingen, Potsdam and DeepGlobe benchmarks. Quantitative and qualitative evaluations exhibit the competitive performance of ICTNet compared to mainstream and state-of-the-art methods. Additionally, the ablation study of DFA and DUP is implemented to validate the effects.

Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination of features. Considering the inherent spectral qualities of RSIs, it is essential to bolster these representations by incorporating the spectral context in conjunction with spatial information to improve discriminative capacity. In this paper, we introduce the spectral–spatial context-boosted network (SSCBNet), an innovative network designed to enhance the accuracy semantic segmentation in RSIs. SSCBNet integrates synergetic attention (SYA) layers and cross-fusion modules (CFMs) to harness both spectral and spatial information, addressing the intrinsic complexities of urban and natural landscapes within RSIs. Extensive experiments on the ISPRS Potsdam and LoveDA datasets reveal that SSCBNet surpasses existing state-of-the-art models, achieving remarkable results in $F_1$-scores, overall accuracy (OA), and mean intersection over union (mIoU). Ablation studies confirm the significant contribution of SYA layers and CFMs to the model’s performance, emphasizing the effectiveness of these components in capturing detailed contextual cues.

Semantic segmentation has been a fundamental task in interpreting remote sensing imagery (RSI) for various downstream applications. Due to the high intra-class variants and inter-class similarities, inflexibly transferring natural image-specific networks to RSI is inadvisable. To enhance the distinguishability of learnt representations, attention modules were developed and applied to RSI, resulting in satisfactory improvements. However, these designs capture contextual information by equally handling all the pixels regardless of whether they around edges. Therefore, blurry boundaries are generated, rising high uncertainties in classifying vast adjacent pixels. Hereby, we propose an edge distribution attention module (EDA) to highlight the edge distributions of leant feature maps in a self-attentive fashion. In this module, we first formulate and model column-wise and row-wise edge attention maps based on covariance matrix analysis. Furthermore, a hybrid attention module (HAM) that emphasizes the edge distributions and position-wise dependencies is devised combing with non-local block. Consequently, a conceptually end-to-end neural network, termed as EDENet, is proposed to integrate HAM hierarchically for the detailed strengthening of multi-level representations. EDENet implicitly learns representative and discriminative features, providing available and reasonable cues for dense prediction. The experimental results evaluated on ISPRS Vaihingen, Potsdam and DeepGlobe datasets show the efficacy and superiority to the state-of-the-art methods on overall accuracy (OA) and mean intersection over union (mIoU). In addition, the ablation study further validates the effects of EDA.

Hyperconcentrated confluent floods in the Middle Yellow River and its tributaries are characterized by the phenomena of extremely high backwater stage, flood water intrusion, and local channel blockage in confluence areas. In order to study the propagation process of hyperconcentrated confluent floods, most existing channel network models need reinforcement in the computation of sediment transport and improvement in the treatment of interior boundary at channel junctions. A river network model for hyperconcentrated confluent floods is proposed based on the finite volume method scheme, in which the study domain is represented by ordinary 1‐D cells and junction cells. As a preprocess step, a bathymetry interpolation algorithm is developed to generate topography data and to extract the stage‐volume relationship for the junction cell. The storage change and sediment transport in the junction cell are calculated in each time step with no need to distinguish convergent or divergent flow pattern, and the solution in each reach can be executed simultaneously. The model was validated by simulating the hyperconcentrated confluent flood events in 1977 and 1966, with the maximum backwater stage and the occurrence time of flood water intrusion in the Lower Weihe River being well predicted. The response was investigated of the Middle Yellow River and Lower Weihe River to the arrangement of peak times of confluent floods, with the critical condition for flood water intrusion and the scenario that achieves the most effective scouring in the downstream channel being identified.

Semantic segmentation of remote-sensing imagery strives to assign a pixel-wise semantic label. Since encoder-decoder networks have demonstrated tremendous success in natural image semantic segmentation, the adoption and extension of this kind of method are transferring such superior performance for the problems in remote-sensing. Facing the high-altitude angle of imaging and complex and diverse ground objects of remote-sensing data, it is necessary to strengthen the features' distinguishability by enhancing the network's capability. Nevertheless, the existing methods suffer from the structural stereotype, leveraging the short-range and long-range contextual information insufficiently. Attempting to address the problems mentioned above, a hierarchical self-attention embedded neural network with dense connection for remote sensing image semantic segmentation (HSDCN) is proposed. In the encoder stage, multiple self-attention modules (SAM) are embedded to model pixel-wise and channel-wise relationships at various scales hierarchically, making the representations more refined and discriminative. Then the dense connections are used to fuse the heterogeneous features. Thus, the network could produce logical and reasonable clues for labeling pixels. The extensive experiments are conducted on ISPRS Vaihingen and Potsdam benchmarks. And the results reveal significant improvements in comparison with other state-of-the-art methods.